Abstract: Embodiments of systems presented herein may identify users to include in a match plan. A parameter model may be generated to predict the retention time of a set of users. A queue of potential users, a set of teammates, and/or opponents may be selected from a queue of waiting users. User information for the set of teammates and/or opponents may be provided to the parameter model to generate a predicted retention time. The set of teammates and/or opponents may be approved if the predicted retention time meets a predetermined threshold. Advantageously, by creating a match plan based on retention rates, the engagement and/or retention level for a number of users may be improved compared to existing multiplayer matching systems.

Abstract: Embodiments presented herein use an audio based authentication system for pairing a user account with an audio-based periphery computing system. The audio-based authentication system allows a user to interface with the periphery device through a user computing device. The user can utilize a previously authenticated user account on the user computing device in order to facilitate the pairing of the audio-based periphery computing system with the user account.

Abstract: Systems and methods for generating a customized virtual character are disclosed. A system may obtain video data or other media depicting a real person, and then may provide the obtained media to one or more machine learning models configured to learn visual appearance and behavior information regarding the particular person depicted in the video or other media. The system may then generate a custom visual appearance model and a custom behavior model corresponding to the real person, which may subsequently be used to render, within a virtual environment of a video game, a virtual character that resembles the real person in appearance and in-game behavior.

Abstract: System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.

Abstract: Systems described herein may automatically and dynamically adjust the amount and type of computing resources usable to execute, process, or perform various tasks associated with a video game. Using one or more machine learning algorithms, a prediction model can be generated that uses the historical and/or current user interaction data obtained by monitoring the users playing the video game. Based on the historical and/or current user interaction data, future user interactions likely to be performed in the future can be predicted. Using the predictions of the users' future interactions, the amount and type of computing resources maintained in the systems can be adjusted such that a proper balance between reducing the consumption of computing resources and reducing the latency experienced by the users of the video game is achieved and maintained.

Abstract: Using voice recognition, a user can interact with a companion application to control a video game from a mobile device. Advantageously, the user can interact with the companion application when the video game is unavailable because, for example, of the user's location. Moreover, machine learning may be used to facilitate generating voice responses to user utterances that are predicted to improve or maintain a user's level of engagement with the companion application, or its corresponding video game.

Abstract: Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.

Abstract: Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.

Abstract: Systems and methods for generating a customized virtual character are disclosed. A system may obtain video data or other media depicting a real person, and then may provide the obtained media to one or more machine learning models configured to learn visual appearance and behavior information regarding the particular person depicted in the video or other media. The system may then generate a custom visual appearance model and a custom behavior model corresponding to the real person, which may subsequently be used to render, within a virtual environment of a video game, a virtual character that resembles the real person in appearance and in-game behavior.

Abstract: Using user-specific prediction models, it is possible to present an individualized view of messages generated by users playing a shared instance of a video game. Further, users with different subjective views of what is offensive may be presented with different forms or annotations of a message. By personalizing the views of messages generated by users, it is possible to reduce or eliminate the toxic environment that sometimes forms when players, who may be strangers to each other and may be located in disparate locations play a shared instance of a video game. Further, the user-specific prediction models may be adapted to filter or otherwise annotate other undesirable messages that may not be offensive, such as a message generated by one user in a video game that includes a solution to an in-game puzzle that another user may not desire to read as it may spoil the challenge for the user.

Abstract: Systems and methods are disclosed for enabling a player of a video game to designate custom voice utterances to control an in-game character. One or more machine learning models may learn in-game character actions associated with each of a number of player-defined utterances based on player demonstration of desired character actions. During execution of an instance of a video game, current game state information may be provided to the one or more trained machine learning models based on an indication that a given utterance was spoken by the player. A system may then cause one or more in-game actions to be performed by a non-player character in the instance of the video game based on output of the one or more machine learning models.

Abstract: Systems described herein may automatically and dynamically adjust the amount and type of computing resources usable to execute, process, or perform various tasks associated with a video game. Using one or more machine learning algorithms, a prediction model can be generated that uses the historical and/or current user interaction data obtained by monitoring the users playing the video game. Based on the historical and/or current user interaction data, future user interactions likely to be performed in the future can be predicted. Using the predictions of the users' future interactions, the amount and type of computing resources maintained in the systems can be adjusted such that a proper balance between reducing the consumption of computing resources and reducing the latency experienced by the users of the video game is achieved and maintained.

Abstract: Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.

Abstract: Embodiments of the systems and methods disclosed herein provide a request distribution system in which a request for resources may be executed by a plurality of workers. Upon receiving a request for resources from a user computing system, the request distribution system may select a subset of workers from the plurality of workers to execute the request within a time limit. Once the workers generate a plurality of outputs, each output associated with a quality level, the request distribution system may transmit the output associated with the highest quality level to the user computing system.

Abstract: System and methods for utilizing a video game console to monitor the player's video game, detect when a particular gameplay situation occurs during the player's video game experience, and collect game state data corresponding to how the player reacts to the particular gameplay situation or an effect of the reaction. In some cases, the video game console can receive an exploratory rule set to apply during the particular gameplay situation. In some cases, the video game console can trigger the particular gameplay situation. A system can receive the game state data from many video game consoles and train a rule set based on the game state data. Advantageously, the system can save computational resources by utilizing the players' video game experience to train the rule set.

Abstract: Sequence predictors may be used to predict one or more entries in a musical sequence. The predicted entries in the musical sequence enable a virtual musician to continue playing a musical score based on the predicted entries when the occurrence of latency causes a first computing system hosting a first virtual musician to not receive entries or timing information for entries being performed in the musical sequence by a second computing system hosting a second virtual musician. The sequence predictors may be generated using a machine learning model generation system that uses historical performances of musical scores to generate the sequence predictor. Alternatively, or in addition, earlier portions of a musical score may be used to train the model generation system to obtain a prediction model that can predict later portions of the musical score.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for interactive computer-operated agents for user engagement in an interactive environment. Computer-operated agents are introduced to help populate a session and are configured to maximize engagement rates among users associated with user-controlled agents. During these interactions, engagement metrics are collected that indicate different interaction rates at different times by the computer-operated agents. The number of popular computer-operated agents (with relatively high interaction rates) can be kept in circulation while some less popular computer-operated agents (with relatively smaller interaction rates) can be kept in circulation for diversity or are purged from circulation.

Abstract: Systems and methods are disclosed for converting a player-controlled character or virtual entity in a video game to at least temporarily be under emulated control when certain criteria is met, such as when the player's device has lost its network connection to a game server. The character or virtual entity may continue to behave in the game in a manner that emulates or mimics play of the actual player until the end of the game session or until the underlying connection problem or other issue is resolved, such that other players participating in the game session have the same or similar gameplay experience as they would have had if the disconnected player had continued to play.

Abstract: Systems and methods are disclosed for enabling a player of a video game to designate custom voice utterances to control an in-game character. One or more machine learning models may learn in-game character actions associated with each of a number of player-defined utterances based on player demonstration of desired character actions. During execution of an instance of a video game, current game state information may be provided to the one or more trained machine learning models based on an indication that a given utterance was spoken by the player. A system may then cause one or more in-game actions to be performed by a non-player character in the instance of the video game based on output of the one or more machine learning models.

Abstract: Sequence predictors may be used to predict one or more entries in a musical sequence. The predicted entries in the musical sequence enable a virtual musician to continue playing a musical score based on the predicted entries when the occurrence of latency causes a first computing system hosting a first virtual musician to not receive entries or timing information for entries being performed in the musical sequence by a second computing system hosting a second virtual musician. The sequence predictors may be generated using a machine learning model generation system that uses historical performances of musical scores to generate the sequence predictor. Alternatively, or in addition, earlier portions of a musical score may be used to train the model generation system to obtain a prediction model that can predict later portions of the musical score.